Zobrazeno 1 - 10
of 33
pro vyhledávání: '"Torsten Enßlin"'
Publikováno v:
Entropy, Vol 26, Iss 11, p 930 (2024)
Nested sampling (NS) is a stochastic method for computing the log-evidence of a Bayesian problem. It relies on stochastic estimates of prior volumes enclosed by likelihood contours, which limits the accuracy of the log-evidence calculation. We propos
Externí odkaz:
https://doaj.org/article/6d5db81fb079433a84565d31e3260b67
Publikováno v:
Entropy, Vol 25, Iss 4, p 652 (2023)
Bayesian imaging algorithms are becoming increasingly important in, e.g., astronomy, medicine and biology. Given that many of these algorithms compute iterative solutions to high-dimensional inverse problems, the efficiency and accuracy of the instru
Externí odkaz:
https://doaj.org/article/29a4d679ffab44ec9b7df7f915d26195
Autor:
Matteo Guardiani, Philipp Frank, Andrija Kostić, Gordian Edenhofer, Jakob Roth, Berit Uhlmann, Torsten Enßlin
Publikováno v:
PLoS ONE, Vol 17, Iss 10 (2022)
The viral load of patients infected with SARS-CoV-2 varies on logarithmic scales and possibly with age. Controversial claims have been made in the literature regarding whether the viral load distribution actually depends on the age of the patients. S
Externí odkaz:
https://doaj.org/article/9ca0c53830134abca9b33d53e641a512
Publikováno v:
The Astrophysical Journal, Vol 955, Iss 1, p 16 (2023)
We present the results of a wideband high-resolution polarization study of Hydra A, one of the most luminous FR I radio galaxies known and among the most well studied. The radio emission from this source displays extremely large Faraday rotation meas
Externí odkaz:
https://doaj.org/article/de2008f876084f3e969b2a046c8850b9
Autor:
Michael M. Foley, Alyssa Goodman, Catherine Zucker, John C. Forbes, Ralf Konietzka, Cameren Swiggum, João Alves, John Bally, Juan D. Soler, Josefa E. Großschedl, Shmuel Bialy, Michael Y. Grudić, Reimar Leike, Torsten Enßlin
Publikováno v:
The Astrophysical Journal, Vol 947, Iss 2, p 66 (2023)
Barnard’s Loop is a famous arc of H α emission located in the Orion star-forming region. Here, we provide evidence of a possible formation mechanism for Barnard’s Loop and compare our results with recent work suggesting a major feedback event oc
Externí odkaz:
https://doaj.org/article/01018909952e47d88f399c71e4e63861
Publikováno v:
Entropy, Vol 24, Iss 12, p 1768 (2022)
Modern communication habits are largely shaped by the extensive use of social media and other online communication platforms. The enormous amount of available data and speed with which new information arises, however, often suffices to cause misunder
Externí odkaz:
https://doaj.org/article/6938ba35a9de48598fffd10a5bcc9374
Autor:
Torsten Enßlin
Publikováno v:
Entropy, Vol 24, Iss 3, p 374 (2022)
Information field theory (IFT), the information theory for fields, is a mathematical framework for signal reconstruction and non-parametric inverse problems. Artificial intelligence (AI) and machine learning (ML) aim at generating intelligent systems
Externí odkaz:
https://doaj.org/article/ca697b35df24477a8184a8f7539dddaf
Publikováno v:
Entropy, Vol 23, Iss 12, p 1652 (2021)
Knowledge on evolving physical fields is of paramount importance in science, technology, and economics. Dynamical field inference (DFI) addresses the problem of reconstructing a stochastically-driven, dynamically-evolving field from finite data. It r
Externí odkaz:
https://doaj.org/article/0eef821ce9f34b528a0465e34984c837
Autor:
Maximilian Kurthen, Torsten Enßlin
Publikováno v:
Entropy, Vol 22, Iss 1, p 46 (2019)
We address the problem of two-variable causal inference without intervention. This task is to infer an existing causal relation between two random variables, i.e., X → Y or Y → X , from purely observational data. As the option to modify a potenti
Externí odkaz:
https://doaj.org/article/6bb4fcda762948e599e20e2924e25443
Autor:
Marijke Haverkorn, François Boulanger, Torsten Enßlin, Jörg R. Hörandel, Tess Jaffe, Jens Jasche, Jörg P. Rachen, Anvar Shukurov
Publikováno v:
Galaxies, Vol 7, Iss 1, p 17 (2019)
The IMAGINE Consortium aims to bring modeling of the magnetic field of the Milky Way to the next level by using Bayesian inference. IMAGINE includes an open-source modular software pipeline that optimizes parameters in a user-defined galactic magneti
Externí odkaz:
https://doaj.org/article/5b121f1c8507438a92e6bf1841ec8e25